Machine learning (ML) has had a “profound” effect on Airbnb’s business growth, according to the company's VP of engineering, Mike Curtis.

Airbnb uses ML to optimize matching between guests and hosts, connecting millions of guests to hosts using personalized criteria:

The company uses a machine-learned search ranking model to personalize results for guests. The model factors in guests' tendencies to click on certain bookings. For example, Airbnb might look at whether customers favor specific types of décor in places they book. The company feeds more than 100 characteristics into the model, which then uses the data to identify patterns and personalize search rankings.

It also uses host preferences to personalize search results for guests, promoting hosts likely to accept the accommodation request. For example, the model factors in whether hosts prefer to book guests on back-to-back dates or enjoy having gaps in between bookings.

In addition, Airbnb uses ML in its predictive pricing model to help hosts price their listings. The model uses historical travel patterns for the area to find the likelihood of any listing being booked at any time. This facilitates the arduous pricing process of looking at supply and demand in an area, among other market factors, and gives hosts a base to price from. If hosts are more eager to book a guest they may lower their price slightly to increase their chances of luring in a customer.

Optimizing matches between hosts and guests will be critical to Airbnb’s success as it continues to grow. The variety in types of accommodations Airbnb has is an advantage, as long as it ensures guests can easily find a host that meets their criteria. And as Airbnb adds to its 3 million current listings, ensuring both guests and hosts are satisfied will become more crucial. If users can find the exact accommodation they are looking for, especially if it is at a cheaper price, they are unlikely to revert to using hotels.